Katalin Szemeredi

Multinational business groups Article (Accepted version) (Refereed)

Original citation: Originally published in Alcácer, Juan and Kogut, Bruce and Thomas,

Catherine and Yin Yeung, Bernard, (eds.) Geography, Location, and Strategy. Advances in Strategic Management,36. Emerald Publishing Limited, Bingley, UK, pp. 87-123. ISBN 9781787142770

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1

Multinational Business Groups

Katalin Szemeredi

London School of Economics CEP

Abstract:

This paper provides a primer on European multinational business groups and

their subsidiaries. Firms in these business groups appear to have higher

sales performance than firms in domestic groups (15% higher). This leads us

to investigate which elements increase the likelihood that a group will

transition towards multinational status. Business groups’ characteristics

matter for foreign acquisition: groups becoming multinational are usually

larger, have a more hierarchical structure with respect to the number of

layers in a group, and are more diverse in terms of sectors. Groups tend to

expand into bordering countries or countries providing particular advantages,

such as a large internal market. The first acquisition is a corporate-level

decision that appears to be made by the group’s controlling firm and is often

a diversification into a different industry.

Acknowledgement: I would like to thank Nitika Bagaria, Johannes Boehm, Esther Ann Boler, Swati Dhingra, John Morrow, Evangelina Pateli, Isabel Roland ,Vincenzo Scrutinio, Tommaso Sonno and Gianmarco Ottaviano and others working at the Centre for Economics Performance at The London School of Economics, Miriam Manchin, Christian Fons Rosen, Giorgio Zanarone and Zsuzsanna Szemeredi for their valuable comments. Contact: K.Szemeredi@lse.ac.uk

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1 Introduction

In recent years, researchers in both economics and finance have

focused on business groups (BGs, hereafter). In this paper, we look at

European business groups across seven years (2001 through 2007) and

study the characteristics of multinational and domestic-only business groups.

First, we show how the sales performance of firms belonging to multinational

groups compares to that of domestic-only groups; second, we look at which

characteristics of business groups make them more likely to transition from a

domestic group towards a multinational status; and, finally, we look at

patterns of first foreign acquisition and try to shed light on possible factors

that can influence groups going multinational.

As this paper looks at business groups (BGs), a formal definition is

provided. A business group (a.k.a a “corporate group”) is defined as a group

consisting of two or more legally independent firms operating in multiple and

often distinct markets, in which affiliates are under the control of one ultimate

owner at the top of the pyramid. Pyramid structures are built through a series

of chains of equity ties from the headquarters to all its subsidiaries (La Porta

et al, 1999; Almeida and Wolfensohn, 2006), in which the ultimate owner

owns the subsidiary either directly—i.e., with direct ownership of its

subsidiary—or indirectly—i.e., with an ownership stake through one or more

intermediate firms. Being a part of a business group allows interaction among

the affiliates, while allowing them to keep their independent status to make

final decisions and access markets. It is this latter property that distinguishes

business groups from multidivisional markets and standalones. The smallest

business group is composed of two firms, with one affiliate and one

headquarters. Multinational business groups have at least one firm from a

different country than the ultimate holder.

Studies have highlighted the importance of business groups in a

number of ways. First, studies dating back as early as the 1980s have shown

that group-affiliated firms are more profitable than standalone companies.

Chang and Choi (1998) showed that the performance of group-affiliated firms

was more profitable than standalone firms in South Korea; other studies have

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shown that business groups are able to offset imperfect institutions and

missing markets (Caves, 1989; Khanna and Yafeh, 2007; Khanna and

Rivkin, 2001; Carney et al., 2011; Mahmood and Mitchell, 2004). Groups also

have shown to be effective in sharing management talent and informational

flows (Khanna and Palepu, 1997) and in redistributing the financial resources

that benefit groups-affiliates’ innovative performance (Belonzon and

Berkovitz, 2010). The correlation among business group structure, vertical

integration and productivity has been analyzed by Altomonte and Rungi

(2013). The management literature has focused primarily on within-firm

structures, mainly in the context of managerial know-how, with firms modeled

as hierarchical organizations of expertise (Garicano, 2000; Garicano and

Rossi Hansberg, 2004, 2006, 2012; Garicano and Hubbard, 2007; Bloom,

Sadun and Van Reenen (2012).

While more and more studies have focused on how groups matter and

why business groups exist, most of their analysis is limited to business group

affiliate performance—that is, comparing standalone firms to group-affiliated

firms. Little has been done to analyze various group structures—in particular,

the differences between multinational and domestic groups. We know that

multinational firms tend to do better and are stronger than national firms, but

the differences between nationals and multinationals at the group level has

not been explored to date, and it is this gap that we aim to fill.

We know that multinational enterprises (MNEs) have attracted

increased attention in the economic literature over the past decade, being at

the center of various fields, such as international trade, international finance,

management and organizational economics. This is not surprising given the

relevance of MNEs in the global economy. According to estimates by Unctad

(2011), multinational firms represent 25% of global GDP and account for

around one third of international trade.

It is well known that enterprises become multinational to gain access

to larger markets, to secure cheaper input for production, and to gain access

to knowledge. Studies have also shown that plants belonging to MNEs differ

from non-MNE plants in both their characteristics and their performance.

These plants tend to be larger and more innovative, to perform better, and to

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pay higher wages. (Doms and Jensen, 1998; Crisculo, Haskel and Slaughter,

2004).

This difference in firm performance of national versus multinational

ownership can be explained in a number of ways: first, if the acquiring foreign

owner is more productive, we observe a positive spillover to subsidiaries;

second, if the foreign firm comes from a technologically advanced country,

the firm enables technology transfer to the subsidiary, hence allowing for

better subsidiary performance (Arnold and Javorcik, 2009; Benfratello and

Sembenelli, 2006; Carluccio and Fally, 2010; Bloom, Sadun and Van

Reenen, 2012). Studies such as Lipsey and Sjoholm (2002) and Girma and

Holger (2007) also find a positive post-acquisition wage effect. All of these

studies provide compelling evidence for the positive effects of foreign

ownership on plant performance. The vital role that multinationals play leads

us to ask: what propels business groups to become multinational?

The contribution of this paper is to bridge the literature on Multinational

Enterprises and that on Business Groups. As previously discussed, the

benefits of belonging to an MNE have been identified: we know why firms

chose to cross borders, and we have an understanding of the benefits of

belonging to BGs. However, little is known about the dynamics of national

and multinational business groups in terms of (i) their respective subsidiaries’

sales performance; (ii) the characteristics that lead a group to transition into a

multinational status; and (iii) how these domestic groups start their first

foreign acquisition in terms of: the countries they decide to enter; the level of

the pyramid ownership structure at which the acquisition takes place (see

Section 2, “Data,” for more details on the definition of pyramid ownership);

and whether they decide to take advantage of comparative advantage by

acquiring a firm in a different sector of their own.

The remainder of the paper is structured as follows. Section 2

provides a detailed analysis of the data used in this paper and includes

descriptive statistics on the period studied. In Section 3, we divide business

groups into two categories, multinational and domestic groups, and look at

differences at both the group level and the subsidiary level. Multinational

groups are larger (in terms of number of firms in a group), more hierarchical

(in terms of the verticality of the group, measured as the distance between

5

the headquarters and the furthest subsidiary in the ownership chain), and

have greater sectoral diversity than national groups. Groups expand primarily

in their home country and expand across countries only progressively.

Subsidiaries belonging to multinational groups have higher employment,

capital and sales than those in domestic-only groups. We conclude this

section by running a simple OLS regression on subsidiary sales, controlling

for group and firm characteristics and including fixed effects. We find that

subsidiaries belonging to multinational business groups have a 15% higher

sale premium and 12% higher value added than those in domestic groups. In

Section 4, we first want to understand whether domestic groups become

stronger once they gain multinational status (treatment effect) or whether the

groups switching to multinational status had different characteristics to start

with that allowed them to become multinational (selection effect); second we

identify possible drivers to multinational status. We find that firms that belong

to switching groups are larger in terms of employment and have higher sales

and value added than those in always-domestic groups even before

becoming multinational. Likewise, on a group level, switching groups have a

different structure from always-domestic groups: they have more affiliates

and higher sectoral diversity and are more hierarchical in terms of the

number of layers in their group. This suggests a strong selection into

becoming multinational. We conclude by using a linear probability model to

assess which group characteristics are associated with a group transitioning

into a multinational status. We find that better-performing and more-capital-

intense groups are more likely to become multinationals, and the probability

of becoming a multinational also increases with the size of the group and with

group hierarchy. Our last section, Section 5, analyzes how a domestic group

becomes multinational by looking at the countries in which domestic groups

first make their first foreign acquisition; the layer of the hierarchy at which

they acquire the foreign subsidiary; and the sector in which the newly

acquired foreign firm is active. We find evidence that (i) most groups acquire

their first subsidiary in neighbouring regions or in regions that are legally and

culturally similar; (ii) that a “corporate-level decision” is made when acquiring

a foreign subsidiary, as the new affiliate comes in at the top layer of the

group, close to headquarters; and (iii) that the first foreign subsidiary is

6

usually from a different sector than the overwhelming part of the group and

from a different sector than the headquarters, suggesting that becoming a

multinational is to take advantage of a new country’s comparative advantage.

To carry out the analysis, we use the Amadeus (Bureau Van Dijk)

dataset, which provides detailed accounting and financial information on

companies and includes ownership data between shareholders and their

subsidiaries.1 Since Amadeus only contains information on direct ownership

links, and not on the entire chain of ownership links, we use the “European

Pyramid Ownership Structures” dataset created by Bena, Fons-Rosen and

Hanousek (2009). By also establishing intermediate ownership links, the

authors develop a dataset based on Amadeus that describes pyramid

ownership structures of business groups (for more details on the construction

of pyramid ownership, see Section 2 “Data”).

2 Data and Descriptive Statistics

2.1 Data

This paper relies on the Amadeus dataset (Bureau Van Dijk), which provides

in-depth annual reports on accounting information and private company

information for approximately 8,000,000 firms from 35 countries in both

Western and Eastern Europe.2 Bureau Van Dijk (BvD) gathers data from a

number of sources, including company registration offices, national statistics

offices and stock exchanges. Amadeus/Orbis also provides information on

comprehensive ownership reporting, each firm’s shareholders and ownership

stakes, and, in particular, each firm’s ultimate owner. From Amadeus, we use

information on firms’ BvD unique identifier, employment, capital (tangible

fixed assets) and turnover (sales).

Although Amadeus has detailed data on direct ownership links, it does

not offer an entire chain of ownership links (direct and indirect) for business

groups and, therefore, fails to provide information on business group

structures. Hence, we supplement Amadeus data with the “European

Pyramid Ownership Structures” dataset developed by Bena, Fons-Rosen,

1 https://amadeus.bvdinfo.com/

2 https://amadeus.bvdinfo.com/

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and Hanousek (2009). This dataset provides pyramid ownership structures of

business groups based on direct ownership links from Amadeus.3 To

construct an annual panel of ownership data, Bena et al. put together

multiple issues of Amadeus DVDs, to construct a pyramid ownership dataset.

They use seven Amadeus DVD updates, each including cross-sectional

ownership data spanning from 2001 to 2007. The authors construct an

algorithm that uses direct ownership links of Amadeus firms to create a

database that describes pyramid ownership structures by providing

intermediate ownership stakes, where ownership is defined as cash- flow

rights. The algorithm works backwards: it begins by taking a firm that does

not own any other firm (hence does not have ownership of any subsidiary)

and then, based on the ownership relation provided by Amadeus, finds the

holders of this firm. The process is repeated until the ultimate owner is

reached (the highest on the pyramid, which has no other holder). With this

recursive method, several other variables can be constructed: the indirect

ownership percentage; the direct ownership percentage; and the aggregate

ownership of the ultimate owner in the subsidiary.4 Further variables

constructed by the authors are the number of steps it takes from the

subsidiary to the ultimate owner (by counting the links of intermediate

owners); the minimum layer at which the subsidiary is present in the group;

the maximum layer at which the subsidiary is present. From these statistics,

we construct our own business conglomerate measures: total levels (layers)

of the business group; the number of affiliates in a group (business group

size); and the number of affiliates on a given layer. These variables will aid

us to understand the pyramidal structure of the group—that is, the group

hierarchy (verticality) in terms of the number of layers in a group and the level

on which the affiliates are present.

3 Data were kindly provided by the authors of the dataset.

4To illustrate, suppose that firm A (the headquarters) has a direct ownership share of 26% in firm C

(subsidiary 1) and has a direct ownership share of 81% in firm B (subsidiary 2). Furthermore, if firm

B has a direct ownership share of 52% in firm C, then the aggregate ownership of firm A in firm C

would be the sum of what A directly owns in C and what it indirectly owns in C (through B). Hence,

Direct: [(26 % ownership of A in C)] + Indirect: [(81% ownership of A over B) * (*52%) ownership

of B over C) = Aggregate ownership of A in C : [26 % direct + (81% *52%) indirect]= 68% .

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2.2 Sample Construction

The original panel of pyramid business groups across seven years contains

over 10,000,000 firm-year observations. To arrive at our final sample, we

consider the following restrictions: (1) In the sample of business groups, to

ensure clear interpretation of control, we impose a 50.01% aggregate

ownership cut-off. Aggregate ownership is the percentage share that the

ultimate holder has in the subsidiary both directly and indirectly. Therefore,

this implies that although the ultimate owner may not formally directly control

the subsidiary, as long as its aggregate ownership exceeds 50.01%, the

ultimate owner qualifies as controlling the subsidiary. Imposing the aggregate

ownership cut-off leads to 3,520,000 observations. (2) A business group or a

subsidiary must be present for at least two years; therefore, those

subsidiaries (and ultimate owners) that are present in only one year are

dropped. (3) We also drop firms owned by individuals (resulting in 2,100,000

observations). (4) The number of employees, the tangible fixed assets (EUR

mil) and the sales (EUR mil) of a firm are variables used in our regressions.

Hence, the requirement that all variables be present for the analysis

substantially reduces the final sample, as only 16% of our observations have

all of these variables present, leaving us with 339,822 observations. (5) In

addition, we drop business groups for which the headquarters’ industry

(NACE 4) is not known. (6) For our analysis, we exclude business groups

that gain and lose multinational status in the sample, as such dynamics

would require a relatively high degree of activity in terms of acquisition and

dismissal of subsidiaries. This could be due, in large part, to misreporting

(when considering such a limited time frame). This reduces the sample by

2%. (7) Moreover, as the focus of the paper is how business groups become

multinational, we do not consider BGs that lost their multinational status (an

additional 0.07% of the sample is dropped). Therefore, we are left with a total

of 268,781 observations as the final sample. These observations belong to

74,213 subsidiaries and 47,948 ultimate holders (BG’s).5

5Each subsidiary belongs to one ultimate owner for two reasons: i) the ultimate owner must own at

least 50.01% of the subsidiary; therefore, each subsidiary can belong to only one ultimate owner in a

given year; and ii) the handful of subsidiaries where the subsidiary switched ultimate owners across

the years is dropped to make the analysis as clean as possible.

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2.3 Descriptive Statistics

In this section, we provide some descriptive statistics on the final sample.

The panel for our analysis ranges from 2001 to 2007 ( Table 1). The number

of business groups in our sample increases with time; however, in 2007, our

sample drops due to the truncated version of the last available year in the

pyramid dataset provided by Bena et al. (2009). The panel contains 180,772

observations for business groups at the year level, for a total of 47,948

unique business groups. In terms of subsidiaries present in the panel, there

are 268,731 observations that correspond to 74,213 unique subsidiaries.

Table 1. Number of Observations per Year

Panel A:

# of Business Groups

year panel Freq Percent

2001 12319 7%

2002 19005 11%

2003 26184 14%

2004 31219 17%

2005 37686 21%

2006 36077 20%

2007 18282 10%

Total 180772

No. of BG 47948

Panel B

# of Subsidiaries

year panel Freq Percent

2001 18456 7%

2002 29792 11%

2003 38464 14%

2004 46367 17%

2005 56462 21%

2006 53044 20%

2007 24442 9%

Total 268731

No. of sub 74213

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Note: The table shows the number of observations per year. Panel A shows the number of business groups present each year. Total refers to the number of business groups in our panel, and No. of BG is the unique number of business group in our panel. Panel B shows the number of subsidiaries present each year. Total is the number of subsidiaries, and No. of sub is the unique number of subsidiaries in our panel. The data come from the Amadeus dataset (Bureau Van Dijk) for years 2001-2007. The sample is restricted to business groups and subsidiaries in which (i) the ultimate holders and subsidiaries were present for at least two years; (ii) the aggregate ownership of the ultimate holder in the subsidiary exceeds 50.01%; and (iii) the information on the number of employees, tangible fixed assets and sales are available for the firm.

Denmark appears to have the largest business groups, and this

seems to be related to the presence of few very large groups at the top of the

distribution. Portugal and Switzerland also have large business groups, but in

their case, the distribution seems entirely shifted towards larger BGs, as

suggested by a median business group size that is almost twice as large than

those of the other countries. The average size of a business group in our

total sample (represented by all 34 countries) is 3.37.

Table 2 provides information on business group size (number of firms

in a group) by country, where the country refers to the headquarters' location.

The sample comprises 34 countries, but for ease of presentation, the table

reports the first 20 countries in terms of the highest concentration of business

groups (47,682 business groups, which account for 98% of business groups

in our total sample). We then give statistics on the 50th and 90th percentiles,

the maximum number of firms in the business group and the total number of

business groups by country of origin (ultimate holder’s country). Finally, our

last column reports the share of total business groups in the sample with

headquarters originating in the given country. As the table reveals, a large

share of our business groups (75.5%) have headquarters in France, Sweden

and Spain. The second column tells us the mean of business group size in

terms of the number of subsidiaries for each country. In terms of mean size,

we cannot conclude that Southern European or Northern European countries

have a larger number of firms within their business groups. Denmark appears

to have the largest business groups, and this seems to be related to the

presence of few very large groups at the top of the distribution. Portugal and

Switzerland also have large business groups, but in their case, the

distribution seems entirely shifted towards larger BGs, as suggested by a

median business group size that is almost twice as large than those of the

other countries. The average size of a business group in our total sample

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(represented by all 34 countries) is 3.37.

Table 2. Average business group size by country (based on Ultimate Holder’s

country)

BG Size

(1) (2) (3) (4) (5) (6)

Top 20 Countries mean p50 p99 max #BG % of BG

France 3.02 2 14 187 18838 39.0%

Sweden 2.98 2 14 157 11329 23.4%

Spain 3.26 2 15 134 6345 13.1%

Germany 3.50 2 15 43 2059 4.3%

Italy 5.46 3 36 142 2005 4.1%

Belgium 4.19 3 21 74 1752 3.6%

Finland 3.14 2 13 100 1505 3.1%

Netherlands 5.56 3 36 237 1021 2.1%

Greece 2.60 2 7 30 445 0.9%

Bulgaria 6.00 2 121 232 392 0.8%

Poland 2.76 2 10 33 316 0.7%

Estonia 2.36 2 5 8 310 0.6%

Switzerland 6.82 4 49 87 296 0.6%

Denmark 11.60 3 185 274 177 0.4%

United Kingdom 2.69 2 11 13 172 0.4%

Austia 2.60 2 10 14 172 0.4%

Croatia 4.93 3 26 59 161 0.3%

Romania 4.48 3 30 72 157 0.3%

Portugal 9.62 5 84 127 143 0.3%

Czech Republic 2.74 2 21 21 97 0.2%

Total (34 countries) 3.37 2 18 331 47948 100.0%

Note: Column (1) shows the mean number of subsidiaries in a business group by county of origin (defined by the country of ultimate holder). Column (2) reports the 50th percentile, column (3) the 90th, column (4) the maximum number of firms in the business group, and column (5) the total number of business groups in the respective country. Finally, column (6) reports the share of total business groups in the sample with headquarters originating from the given country. The source, data and sample used are the same as described in Table 1. There are a total of 34 countries in our sample, of which, for ease of presentation, the table reports the first 20 countries in terms of the highest concentration of business groups (47,692 business groups which account for 98% of all business groups). Total represents the statistics for all our 34 countries in our sample (47948 business groups).

Table 3 reports the size of the groups (number of firms in a group) for

the pooled sample. There is a large difference in the number of firms per

group: the majority (63%) of our sample is represented by small groups,

composed of two firms (an ultimate holder and its affiliate); 26% are

composed of three or four firm groups, while the remaining categories make

up the remaining 11% of our business groups.

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Table 3. Size Category for all Business Groups.

# Affiliates No. of BG %

1 30273 63%

2-3 12337 26%

4-5 2875 6%

6-20 2263 5%

21-50 156 0%

51-200 43 0%

201-500 1 0%

Total 47948

Note: The table reflects the size distribution of business groups in our sample. Size is represented in categories by the number of subsidiaries in a group ( # of Affiliates). No. of BG shows the total number of business groups by size category. % shows the share of business groups found in the respective size category. The source, data and sample used are the same as described in Table 1.

Differences in business group size are reflected, in part, by the degree

of hierarchy in the pyramid of groups defined as the number of layers, which

is described in Table 4 (for definition of layers, turn to the beginning of this

section). The majority of our groups have one layer (all subsidiaries being

directly under the headquarters). However, even if the group is composed of

more firms, subsidiaries seem to be concentrated at the first or second level,

as the share of groups with only one layer is larger than the share of groups

with only one subsidiary. This suggests that, generally, the group’s expansion

is a centralized decision, and only for very large groups do subsidiaries play

an active part in its expansion.

Table 4. Group Distribution by Hierarchy in the Pyramid

Total Layers

# BGs %

1 39128 82%

2 6736 14%

3 1407 3%

4 404 1%

(5-10) 265 1%

>10 8 0%

Total 47948 100%

Note: Total Layers reflects the degree of hierarchy of the business group. 1 layer defines a flat business group, where the subsidiaries are directly under the ultimate holder (1 distance away). 2 and more layers imply that there are subsidiaries that are not directly below the

13

ultimate holder—that is, they are at least 2 distances away from the ultimate holder, with subsidiaries found between the ultimate holder and the respective subsidiary. # BGs are the number of groups by category of hierarchy. % represents the share of the total business groups in the sample belonging to the respective category. Source, data and sample used are as described in Table 1.

Lastly, we look at the industry diversity of groups. For a sector

diversity group measure, the affiliates’ 4-digit NACE is taken to construct a

diversity index using the Herfindahl-Hirschman index (HHI).6 As we are

interested in the sectoral diversity of the group, the 1-HHI index is taken. In

general, sector diversification increases with the number of firms in the group

(apart from the last category—however, this statistic is based on few

observations). As Table 5 shows, the larger groups have more scope for

diversifying their activities and might be willing to engage in vertical

integration, thus including in the group suppliers that might belong to different

sectors.

Table 5. Group Level Sectoral Diversity

Sectoral Diversity (NACE4) by No of Affiliates in a BG

#Affiliates Sectoral Diversity

1-5 0.04

6-20 0.23

21-50 0.27

51-200 0.37

Total Average 0.05

Note: # Affiliates reflects the number of firms in a business group. Sectoral Diversity is calculated as the average number of sectors that the business groups are active in by country of headquarters. Sectors are defined at the NACE 4 level. Source and sample are constructed as described in Table 3.

3 Multinational Groups vs Domestic Groups

We further our analysis of business groups by looking at the differences in

the characteristics of multinational and non-multinational groups. The first

part of this section provides details on the sample composition in terms of

group categories (never multinational, always multinational) and presents

6 HHI is the Herfindahl-Hirschman Index, which is measured as the sectoral concentration (based on

4-digit NACE) in a business group. This is calculated by taking the sum of the sectoral shares squared

in a business group. Given that we subtract HHI, concentration index, from 1, a diversity index is

created.

14

descriptive statistics regarding the characteristics of these groups and of their

respective subsidiaries. The second part focuses on the sales performance

premium of firms in multinational groups accounting for differences in

observables. This allows us to isolate the pure multinational effect from

compositional changes; for example, firms in multinational groups might be

larger to begin with, and, hence, a pure sales comparison between the two

categories would not be appropriate.

3.1 Multinational vs Domestic Group Characteristics

We introduce simple summary statistics of our multinational and national

groups in our sample. Table 6 gives brief summary statistics of the

percentage of domestic versus multinational groups by country of origin

(defined as the country where headquarters are located). In our sample, the

countries with the highest share of multinational groups are Germany, Italy,

Belgium, and the Netherlands. While Denmark and Portugal have only

multinational groups, these countries represent just a small fraction of our

business groups. Business groups with headquarters in Bulgaria, the United

Kingdom and Austria are almost all domestic, but, again, these countries

represent only a small proportion of our business groups—probably due to

the limitation of accounting and financial information. Most of the business

groups in France, Sweden and Spain are also domestic; however, given the

high share of groups that they represent, even the small share of

multinational groups amounts to a sizable number of the total multinational

groups in our sample.

Table 6. Total No. of Domestic and Multinational groups by country

No. of Business Groups by Country of HQ

(1) (2) (3) (4) (5)

Top 20 countries Total Groups Non-MN Share MN Share

France 18838 18590 99% 248 1%

Sweden 11329 10743 95% 586 5%

Spain 6345 6231 98% 114 2%

Germany 2059 1608 78% 451 22%

Italy 2005 1423 71% 582 29%

Belgium 1752 1312 75% 440 25%

15

Finland 1505 1429 95% 76 5%

Netherlands 1021 554 54% 467 46%

Greece 445 435 98% 10 2%

Bulgaria 392 392 100%

Poland 316 308 97% 8 3%

Estonia 310 293 95% 17 5%

Switzerland 296 9 3% 287 97%

Denmark 177 177 100%

United Kingdom 172 170 99% 2 1%

Austria 172 171 99% 1 1%

Croatia 161 95 59% 66 41%

Romania 157 138 88% 19 12%

Portugal 143 143 100%

Czech Republic 97 87 90% 10 10%

Total (34 countries) 47692 43988 92.23% 3704 8%

Note: Column (1) shows the total number of groups by country. Of these, Column (2) Non-MN reports the number of groups that are domestic by country, and column (4) MN reports the total number of groups that are multinational. Columns (3) and (5) report the shares of domestic versus multinational groups by country. There are a total of 34 countries in our sample, of which, for ease of presentation, the table reports the first 20 countries in terms of the highest concentration of business groups (47,692 business groups, which account for 98% of all business groups). Total represents the statistics for all 34 countries in our sample (47,948 business groups). The data come from the Amadeus dataset (Bureau Van Dijk) for years 2001-2007. The sample is restricted to business groups and subsidiaries in which (i) the ultimate holders and subsidiaries were present for at least two years; (ii) aggregate ownership of the ultimate holder in the subsidiary exceeds 50.01%; and (iii) information on the number of employees, tangible fixed assets and sales are available for the firm.

It is important to note that the size of the business groups (number of

firms in a group) is consistently larger when groups are multinational as

opposed to domestic, irrespective of the country considered (Table 7).

Moreover, looking at the 99th percentile, respectively, we see that the largest

multinational groups are substantially larger than their domestic counterparts

for each country in our sample.

Table 7. Business Size Category by Country of HQ

BG Size: # of Affiliates in a Group

Country of HQ (top 20 by BG concentration)

Domestic Multinational

(1) (2) (3) (4)

Mean Size 99th Mean Size 99th

France 2.7 10 9.8 81

Sweden 2.6 9 7.4 60

Spain 2.9 11 6.1 38

Germany 2.8 11 3.5 12

16

Italy 4.2 21 5.8 43

Belgium 3.5 14 5.3 30

Finland 2.8 10 4.7 43

Netherlands 3.5 17 6.5 48

Greece 2.4 7 3.9 8

Bulgaria 5.1 83

Poland 2.5 7 4.4 10

Estonia 2.2 5 2.3 5

Switzerland 3.6 12 5.5 30

Denmark 9.7 185

United Kingdom 2.5 10 4.5 5

Austria 2.5 10 2.0 2

Croatia 3.4 18 5.1 21

Romania 3.3 15 9.5 72

Portugal 8.3 63

Czech Republic 2.4 8 4.4 21

Total (34 countries) 2.8 11 6.2 48

Note: Column (1) represents the average number of subsidiaries per group for domestic groups, and column (2) represents the 99th percentile broken down by country of headquarters. Similarly, for multinational groups, columns (3) and (4) represent the average number of subsidiaries per group and the 99th percentile, respectively, by country of headquarters. Source, data and sample used are as described in the Note of Table 6.

Difference in size is also related to differences in other dimensions,

such as the hierarchy in the pyramid structure of the group and sectoral

diversification. Table 8 shows that groups that are cross-national also have a

higher level of sectoral diversity—that is, their subsidiaries belong to more

sectors. Similarly, multinational groups are more hierarchical and span more

layers (subsidiaries can be farther from their) than their national counterparts

within each country. With the systematic differences between multinational

and domestic groups across countries, we can safely claim that these

differences are not country-specific.

Table 8. Sectoral Diversity (mean), # of Layers (mean) of BGs by Country of HQ.

(1) (2) (3)

Sector(NACE 4) # Layers #Cntry

Country of HQ Non-MN MN Non-MN MN MN

France 1.2 2.0 1.0 1.3 1.3

Sweden 1.1 1.6 1.0 1.2 1.2

Spain 1.2 1.9 1.0 1.2 1.2

Germany 1.1 1.2 1.1 1.3 1.1

Italy 1.2 1.3 1.0 1.1 1.2

17

Belgium 1.3 1.4 1.1 1.2 1.2

Finland 1.2 1.6 1.0 1.1 1.3

Netherlands 1.1 1.3 1.2 1.3 1.2

Greece 1.1 1.2 1.0 1.0 1.0

Bulgaria 1.3 1.0

Poland 1.1 1.0 1.0 1.3 1.0

Estonia 1.1 1.1 1.0 1.1 1.1

Switzerland 1.1 1.2 1.0 1.2 1.2

Denmark 1.2 1.7 1.3 United Kingdom

1.1 1.4 1.0

Austria 1.1 1.2 1.1 1.3 1.1

Croatia 1.1 2.0 1.0 1.5 1.0

Romania 1.1 1.0 1.0 1.0 1.0

Portugal 1.1 1.4 1.0 1.3 1.1 Czech Republic

1.1 1.6 1.0 1.1 1.1

Total (34 countries)

1.1 1.4 1.0 1.2 1.2

Note: The table reflects the diversity characteristics for domestic (Non-MN) and multinational (MN) groups by country of headquarters. Domestic groups are those in which all firms belonging to a group are from the ultimate holder’s country, while multinational groups are those in which at least one firm comes from a different country than the ultimate holder. Column (1) shows the average sectoral diversity of firms belonging to domestic and multinational business groups by country of headquarters, where sector is at the NACE 4 level. Column (2) reflects the average number of layers business groups have, and column (3) shows the average number of countries present in groups by country of headquarters. Source, data and sample used are as described in the Note of Table 6.

Multinational groups expand across countries progressively and select

countries according to expected cost of entry and potential benefit. On the

one hand, multinational groups could expand first into neighbouring

countries, as this kind of expansion probably involves lower transaction

costs. On the other hand, a group might be willing to face higher costs in the

presence of higher expected revenues. Countries with especially larger

markets, for example, might be more attractive to groups eager to get direct

access to consumers. In order to shed light on this aspect, it is worth

explicitly examining the country composition of subsidiaries. This is done in

Table 9, which reports the mean number of subsidiaries for each country. As

seen on the diagonal of the table, as expected, in all the countries, business

groups tend to expand in their home country primarily. This appears

reasonable, as a group would tend to consolidate its structure in its home

18

country, where transaction costs are lowest, before moving into different

countries.

Table 9. BG’s country presence

Note: The table reflects the expansion of groups by country of headquarters (HQ) to countries of subsidiaries (sub). The table shows the average number of subsidiaries for each country given the ultimate holder’s country. The table reports the first 20 countries in terms of the highest concentration of business groups (47,692 observations which account for 98% of all business groups in our sample). The data come from the Amadeus dataset (Bureau Van Dijk) for years 2001-2007.

Previous steps have shown that multinational groups tend, at the

aggregate level, to be different from domestic groups along several

dimensions. Our analysis, however, has neglected possible differences at the

subsidiary level. Firms belonging to multinational business groups could be

larger and more profitable, thus allowing the headquarters to face the cost of

making the group multinational. We address this question in Table 10, which

compares affiliate-level size and profitability measures across our groups of

firms by country of headquarters’ origin. More specifically, we look at whether

subsidiaries belonging to multinational groups are, on average, statistically

different in terms of employment, capital and sales to subsidiaries in national

groups. For each characteristic, the first two columns report averages for

Sub

HQ AT BE BG CH CZ DE DK EE ES FI FR GB GR HR IT NL PL PT RO SE

Austria AT 0.8 0.2 0.1 0.3 0.2 0.3 0.1 0.1 0.6 0.3

Belgium BE 0.1 1.5 0.1 0.3 0.1 0.1 0.2 0.2 0.2 0.3 0.1 0.2 0.8 0.8 0.1 0.5 0.3

Bulgaria BG 2

Switzerland CH 0.4 0.7 0.7 0.5 0.5 0.2 0.3 0.1 0.4 0.6 0.1 0.2 0.2 0.3 0.4 0.3 0.2 1

Czech Rep. CZ 1.1 0.1 0.1 0.2 0.5

Germany DE 0.2 0.5 0.1 0.1 1.6 0.3 0.8 0.3 0.2 0.3 0.5 0.2 0.8 0.4 0.8 0.7

Denmark DK 0.1 0.7 0.4 0.1 0.5 0.2 0.1 0.3 0.2 0.1 0.2 0.3 0.1 0.7 0.8

Estonia EE 1.2 0.3

Spain ES 0.1 0.1 1.7 0.2 0.1 0.3 0.1 0.1 0.1

Finland FI 0.3 0.5 0.5 0.1 1.5 0.3 0.1 0.3 0.6 0.1 0.5

France FR 0.2 0.1 0.7 1.6 0.3 0.1

UK GB 0.6 0.9 0.1 0.3 0.6 0.7 0.5 0.6 0.3 0.1 0.6 0.1 0.1 0.1

Greece GR 0.2 1.4 0.9

Croatia HR 1.4

Italy IT 0.2 0.6 0.7 0.5 0.2 0.1 0.5 0.4 1.3 0.1 0.1 0.1 0.5

Netherlands NL 0.1 0.1 0.3 0.4 0.6 0.7 0.2 0.9 0.2 0.2 0.4 0.7 0.7 0.6 0.2 0.1 0.2

Poland PL 0.3 1.3

Portugal PT 0.8 0.4 0.1 1.3

Romania RO 1.7

Sweden SE 0.2 0.1 0.6 0.2 0.1 0.5 0.1 0.2 1.3

Mean number of subsidiary firms per country of HQ

19

firms, whereas the last column for each set of firm characteristics reports the

p-values for the differences in averages. Subsidiaries of multinational groups

are larger in terms of both capital and employment. They also have higher

sales, which suggests a possible higher profitability; however, this could be

related to differences in the other observables or to the group structure. We

address this concern more formally in the next section in a regression

framework.

Table 10. Subsidiary Differences by Non-MN and MN Groups by Country of HQ

Note: Table shows whether subsidiaries belonging to multinational groups are, on average, statistically different to subsidiaries in national groups. Variables tested are employment,

Country of HQ Domestic Multinational p-val Domestic Multinational p-val Domestic Multinational p-val

France 44.0 192.0 0 0.6 7.9 0 7.0 35.4 0

[6.16] [29.83] [.03] [3.8] [.23] [4.84]

Sweden 16.2 98.5 0 2.1 4.9 0 3.3 26.7 0

[.48] [9.81] [.23] [1.1] [.11] [2.78]

Spain 46.0 292.6 0 2.2 15.2 0 8.5 92.3 0

[3.17] [71.79] [.16] [5.14] [.67] [21.18]

Germany 121.2 191.1 0 5.5 6.1 0 25.8 41.7 0

[5.99] [25.93] [.79] [1.02] [1.83] [6.13]

Italy 104.0 103.0 0 7.8 5.8 0 30.9 27.5 0

[6.63] [14.08] [2.17] [.95] [1.76] [3.58]

Belgium 61.4 83.5 0 2.6 3.2 0 17.4 27.3 0

[3.65] [16.14] [.22] [.61] [.9] [6.32]

Finland 37.8 142.9 0 2.0 3.8 0 8.0 28.1 0

[3.27] [33.15] [.48] [.95] [.86] [6.4]

Netherlands 69.2 198.8 0 2.7 10.0 0 20.0 57.1 0

[5.44] [32.43] [.7] [1.59] [1.7] [8.95]

Greece 60.0 80.6 0 3.5 3.7 0 6.1 14.4 0

[12.71] [44.96] [.71] [2.63] [.57] [6.79]

Bulgaria 133.9 . 0 1.7 . 0 3.3 . 0

[13.26] [.] [.3] [.] [.61] [.]

Poland 187.4 111.3 0 2.3 4.5 0 12.2 14.7 0

[26.36] [40.43] [.33] [3.09] [1.97] [7.61]

Estonia 71.1 34.4 0 2.1 0.5 0 5.3 5.2 0

[13.31] [8.45] [1.16] [.16] [.9] [1.12]

Switzerland 128.8 127.5 0 37.6 4.6 0 34.3 29.7 0

[53.43] [16.43] [18.5] [.76] [15.48] [3.55]

Denmark 55.0 0 2.2 0 15.4 0

[7.94] [.55] [.] [2.02]

United Kingdom . 84.3 0 . 3.3 0 . 34.3 0

[.] [13.55] [.] [.78] [.] [11.99]

Austria 218.3 160.2 0 18.5 8.4 0 46.3 31.9 0

[39.68] [37.09] [7.74] [3.4] [7.97] [5.48]

Croatia 191.1 528.0 0 16.1 63.0 0 12.4 46.0 0

[53.65] [197.55] [8.20] [38.72] [3.1] [23.23]

Romania 92.7 83.0 0 0.7 0.5 0 1.1 1.9 0

[22.37] [.] [.22] [.] [.28] [.]

Portugal 208.6 627.1 0 4.4 57.2 0 11.9 92.5 0

[31.73] [294.94] [1.1] [33.24] [1.7] [43.18]

Czech Republic 208.6 627.1 0 4.4 57.2 0 11.9 92.5 0

[310.85] [1251.33] [10.81] [141.03] [16.7] [183.21]

Employment Sales Capital

Subsidiary Characteristics belonging to Domestic and Multinational Groups

20

capital and sales. For each characteristic, the first two columns report averages for firms, whereas the last column for each set of firm characteristics reports the p-values for the differences of average. The table reports these results for firms belonging to the first 20 countries in terms of the highest concentration of business groups. The data come from the Amadeus dataset (Bureau Van Dijk) for years 2001-2007.

3.2 Subsidiary sales performance in Multinational versus

Non-Multinational groups.

Our first empirical strategy focuses on understanding whether a sales

premium of subsidiaries in multinational groups, as opposed to those in

domestic-only groups, is due to differences in group characteristics. We

control for different organizational characteristics that we previously found for

multinational and domestic groups (hierarchy, size and sectoral diversity of

groups). We compare subsidiaries that belong to multinational groups to

subsidiaries in domestic-only groups. The first part of the analysis is based

on a classical Cobb-Douglas production function. This specification describes

the effect of belonging to a multinational group on subsidiary sales.

lnYigt =

β0 + β1MNgt + β2lnLigt + β3lnKigt + ∑ 𝛾j𝑘

𝑗=1Layergtj +

∑ 𝛼j𝑘

𝑗=1BG_sizegtj + β4DIV_Sectorgt + 𝜆t + εigt ,

(1) where 𝑌𝑖𝑔𝑡, measures the sales performance of subsidiary i belonging to

business group g at time t, as in Bloom, Sadun, and Van Reenen (2012a).

For estimating the production function, we use standard input variables: 𝐿𝑖𝑔𝑡

and 𝐾𝑖𝑔𝑡, which are labour and capital of subsidiary i. Our variable of interest

is MNgt, a dummy variable taking on value 1 if business group g is

multinational at time t. Our coefficient of interest is β1. A positive coefficient of

β1 would imply that being part of a multinational group is correlated with

higher subsidiary sales. We control for different group characteristics found in

the previous section for multinational and domestic-only groups. We control

for hierarchy in the pyramid structure of a group by including a set of controls

for the total number of layers in a group, Layergtk. These allow for a richer

description of the effects of the hierarchy on sales performance, allowing for

21

a clearer identification of non-linearities such as those identified by Altomonte

et al. (2013). Similarly, we control for the effects of the size of the group using

a set of dummy variables identifying different size classes based on the

number of firms in group g at time t, 𝐵𝐺_𝑠𝑖𝑧𝑒𝑔𝑡𝑗. As multinational groups are

more likely to be large, we introduce the size control to avoid a biased

multinational coefficient. 𝐷𝐼𝑉_𝑆𝑒𝑐𝑡𝑜𝑟𝑔𝑡, the diversity of the sector of the group

(identified by NACE 2), is also included to control for the level of sectoral

diversification within a group. The sectoral diversity of a group might be

related to its degree of vertical integration or to its level of diversification,

which can lead to a higher level of sales if sectors in the group are

characterized by synergies. Furthermore, year fixed effects 𝜆t are included to

control for common time shock. Finally, εgt is a random noise error term at

the group level at time t.

We find that firms belonging to a multinational group have 15-

percentage-point higher sales than those in non-multinational groups after

controlling for firm characteristics, group characteristics and country/sector

fixed effects.

The results in Table 11 show, on an affiliate level, the effect of being

in a multinational group on subsidiary performance (log of subsidiary sales),

accounting for different group characteristics. Column (1) shows the cross-

sectional effect of belonging to a multinational group and finds that being in

such a group is positively correlated with firm sales. Firms belonging to

multinational business groups have about 36% higher sales than those in

national groups (without the inclusion of fixed effects). Column (2) introduces

year fixed effects to control for yearly shocks; headquarters sector fixed

effects to ensure that it is not the headquarters’ industry characteristic that is

driving the result; location fixed effects. The result still shows 19% higher

sales for firms belonging to multinational groups. Column (3) includes fixed

effects for country and sector composition and interactions between country

and sector fixed effects to allow for a more flexible heterogeneity, still

resulting in 15% higher sales for firms belonging to multinational groups.

Although the multinational premium drops considerably (by approximately

one half), it is still substantial and highly statistically significant. More

hierarchical groups with more layers are also correlated with higher

22

subsidiary sales. Finally, this result is confirmed using a more refined

measure in column (4): firms in multinational business groups have 12%

higher value added ceteris paribus. This is in line with the previous literature

on multinationals, which points out the better performance of firms belonging

to multinational enterprises (Doms and Jensen, 1998; Crisculo, Haskel and

Slaughter, 2004).

Table 11. Subsidiary sales and value added in Multinational Groups

Subsidiary sales and value added in a Multinational Group

(1) (2) (3) (4)

Variables Sales Sales Sales Value Added

Multinational 0.359*** 0.190*** 0.147*** 0.121***

-0.035 -0.016 -0.014 -0.01

Subsidiary L (log) 0.757*** 0.775*** 0.779*** 0.757***

-0.005 -0.005 -0.004 -0.003

Subsidiary K (log) 0.135*** 0.136*** 0.134*** 0.152***

-0.003 -0.003 -0.003 -0.002

Affiliates: 2-3 0.117*** 0.061*** 0.072*** 0.031***

-0.011 -0.009 -0.008 -0.006

Affiliates: 4-5 0.137*** 0.061*** 0.092*** 0.051***

-0.018 -0.015 -0.013 -0.009

Affiliates: 6-20 0.168*** 0.093*** 0.157*** 0.088***

-0.026 -0.022 -0.017 -0.013

Affiliates: 21-50 0.106 0.100* 0.226*** 0.178***

-0.069 -0.054 -0.04 -0.029

Affiliates: 51-200 -0.689** -0.205 0.286*** 0.251***

-0.282 -0.182 -0.067 -0.049

Affiliates: 200+ -2.060*** -0.357** 0.098 -0.077

-0.347 -0.159 -0.132 -0.116

No. Layers 2 0.148*** 0.130*** 0.116*** 0.115***

-0.014 -0.011 -0.009 -0.007

No. Layers 3 0.171*** 0.179*** 0.165*** 0.200***

-0.027 -0.022 -0.018 -0.013

No. Layers 4 0.203*** 0.219*** 0.193*** 0.206***

-0.054 -0.039 -0.024 -0.02

No. Layers 5 0.176* 0.229*** 0.181*** 0.256***

-0.1 -0.066 -0.034 -0.028

No. Layers 6 + 0.411*** 0.339*** 0.187*** 0.275***

-0.14 -0.097 -0.046 -0.038

Industry Diversity -0.166*** -0.03 -0.042** -0.012

-0.034 -0.026 -0.02 -0.015

Obervations 267,027 267,027 267,027 223,468

23

R-Squared 0.663 0.757 0.766 0.836

Year FE NO YES YES YES Sector FE NO YES YES YES

Country FE NO YES YES YES

Country Comp NO NO YES YES

Sector Comp NO NO YES YES

CountryXSector FE NO NO YES YES

Note: The statistical significance is as follows: * significant at 10% ; ** significant at 5%, *** significant at 1%. The table shows on an affiliate level the effect of being in a multinational group on subsidiary performance (log of subsidiary sales), accounting for different group characteristics. Multinational is a dummy variable that takes on a value of 1 if firm i belongs to a multinational group at time t. The dependent variable is (log of) subsidiary sales. (Subsidiary_L) is the log of the number of employees of the subsidiary; Log (Subsidiary_K) is the log of capital of the subsidiary; Affiliates, is the number of subsidiaries in a business group; No. of Layers is the number of layers of the business group; and Industry Diversity is an index measuring the diversity of the group based on NACE 4. Standard errors are clustered at the Headquarters level.

4 Becoming a Multinational Group

We previously showed that multinational groups have more industry diversity

and are more hierarchical than national groups. Given the sales premium of

subsidiaries in multinational groups, even after controlling for group

characteristics, it is imperative to understand whether domestic groups

become stronger after gaining their multinational status (treatment effect), or

whether these switchers have different characteristics that allow them to

become multinational (selection effect). We first examine whether switchers

already look different from always-domestic groups before their first

international move, and, second, we identify possible drivers to multinational

status.

4.1 Domestic Groups that make a foreign acquisition

Until this point in our analysis, we have concentrated on two large groups:

multinational and domestic. As Table 12, shows, domestic groups can be

further divided into two groups: always-domestic (never change to

multinational status) and those that went through a change by acquiring a

foreign firm.

24

Panel A of Table 12 shows that 12% are always multinational, that the

overwhelming majority, 86%, remain domestic and never switch, and 3% are

domestic groups that switch status at some point. One can draw a similar

conclusion by looking at Panel B: throughout our entire study period, 17%

belonged to groups that were multinational. Meanwhile, 80% of our firms

belong to groups that remained domestic, while a small share of firms, 3%,

belonged to groups that experienced a change in status.

Table 12. Business Group Status

Panel A # Business Group

Multinational Domestic

Always Never Switchers Total

3789 43237 922 47948

12% 86% 3% 100%

Panel B # Subsidiary

Multinational Domestic

Always Never Switchers Total

8759 63555 1899 74213

17% 80% 3% 100%

Note: Panel A shows the division of business groups, while Panel B shows the division of subsidiaries that are national versus domestic. Multinationals are those that have always been multinational throughout our sample, identified as Always. Domestic status is further divided into two groups: Never—that is, never changed to multinational status—and those that went through a switch by acquiring a foreign firm, identified as Switchers. These are constructed based on our 47,948 business groups that have, in total, 74,213 subsidiaries. The data comes from the Amadeus dataset (Bureau van Dijk) for years 2001-2007.

We now narrow our analysis to the two domestic groups: always-

domestic and those that were domestic and switched status. Table 13 shows

a comparison of the subsidiary firms of switching groups and always-

domestic groups, using a simple average comparison. The first two columns

report averages for firm characteristics, while the last two columns report p-

values of t-test for differences in averages between the two categories:

switchers and domestic groups (column 3). There appear to be important

differences between domestic subsidiaries belonging to groups that will

switch to a multinational status (these firms are still considered in their

domestic state), and domestic subsidiaries that remain in national groups.

25

On average, firms in groups that switch employ more people than do firms in

domestic groups. In addition, they also have higher productivity, as proxied

by sales and value added per worker. This is consistent with models such as

those of Melitz and Ottaviano (2008), where access to a foreign market is

related to a productivity threshold rule. By the same token, in this setting,

groups may become multinational after the average productivity of its

subsidiaries is sufficient to offset the costs of internationalization. Moreover,

on a group level, switching groups are structurally different from always-

domestic groups: they have more affiliates in their group, have higher

sectoral diversity and are more hierarchical in terms of the number of layers

in a group.

Table 13. Firm and Group Characteristics: Always-Domestic vs Switchers

(1) (2) (3)

Domestic Switchers p-value:

Sub

sid

iary

-Le

vel Subsidiary Sales/L 0.3 0.4 0.0

Subsidiary L 44.9 119.1 0.0

Subsidiary K/L 0.1 0.1 0.8

Subsidiary Sales 7.7 27.9 0.0

Subsidiary VA 2.1 7.1 0.0

Subsidiary VA/L 0.1 0.1 0.0

Gro

up

- Le

vel Business Group

Size 2.8 4.5 0.0 Number of Sectors in BG 1.1 1.4 0.0 Layers of BG (mean) 1.0 1.1 0.0

Note: The table compares the subsidiary firms of always-domestic groups (Domestic) and switching groups (Switchers) in our sample, using a simple average comparison. Column (1) and column (2) report averages for firm characteristics for the two groups, respectively. The upper panel does the analysis at the subsidiary level, while the lower panel does it at the group level. Column (3) reports p-values (p-value) of the t-test for differences in averages

between the two categories: switchers and domestic groups. Data are from the Amadeus (Bureau Van Dijk) dataset, 2001-2007, and the subsample considered is the one described in Table 12. However, only two types of business groups are considered from this subsample—those that become multinational and those that were never multinational (groups always having a multinational status in the study period are dropped). This leads to 44,159 business groups, which have in total 65,454 subsidiaries.

26

To evaluate which group characteristics are associated with a group

transitioning into multinational status, we rely on equation (2). This

specification aims to identify which business groups are more likely to

become multinational. The two groups in our regression are those that are

never multinational and those that become multinational. A linear probability

model is used to assess the probability of the group becoming multinational

given its characteristics. Our specification is:

BecomeMNg =

β0 + β1avglnLg + β2avglnK/Lg + β3avglSalesg + ∑ 𝛾j𝑘

𝑗=1Layergj +

∑ 𝛼j𝑘

𝑗=1BG_sizegj + β4Industry Diversityg+𝜆t + 𝜙g + 𝜓g + εg,

(2)

where the dependent variable, 𝐵𝑒𝑐𝑜𝑚𝑒𝑀𝑁𝑔, is a dummy variable taking on a

value of one if business group g becomes a multinational at any point during

our sample period. 𝑎𝑣𝑔𝑙𝑛𝐿𝑔 is the average log employment of a subsidiary in

business group g; 𝑎𝑣𝑔𝑙𝑛𝐾/𝐿𝑔 is the average log capital intensity of a

subsidiary for group g, while 𝑎𝑣𝑔𝑙𝑆𝑎𝑙𝑒𝑠𝑔 is the average log of sales of a

subsidiary for group g. Similar to specification (1), we include a set of

dummies for the number of layers in the group, a set of business group size

dummies, and a variable to control for the sectoral diversity of a group.

Included also are controls, (i) 𝜆t for the year in which the group appears in the

panel 7 to account for any year shocks; (ii) for the industry of the company

headquarters, 𝜙g, as the headquarters’ sector can influence business

strategies on how to expand the group, which, in turn, can be correlated with

becoming a multinational; and (iii) headquarters county, 𝜓g, which allows us

to control for the possibility of different patterns of internationalization

according to country. We also include (iv) dummies for sector composition;

and, finally, (v) interaction between country and sectors fixed effects to allow

7 For this estimation, there is only one observation for each business group. We take the first year in

which the BG is present in our panel.

27

for a certain degree of heterogeneity across countries. The results of

equation (2) will allow us to describe which characteristics of a group seem to

matter for its transition into a multinational group.

Groups that become multinational are compared strictly with groups

that are never multinational (always-multinational groups are excluded in this

setting). On a group level, the results in Table 14 suggest that better-

performing and more-capital-intense groups (proxied by the average log of

sales and log of capital intensity for the subsidiaries in these groups) are

more likely to become multinational: a one-percentage-point increase in

average sales for subsidiaries in the group leads to a 0.008-percentage-point

increase in the probability of becoming a multinational group (the magnitude

of the effect is, however, small). The probability of becoming a multinational

group increases with the size of the group (number of firms in a group), as

well as with group hierarchy (measured as the number of layers in a group).

These results clearly show that domestic groups in our sample that

become switchers already look different than always-domestic groups prior to

their first international acquisition; therefore, our findings suggest that there is

a strong selection into becoming multinational.

Table 14. Probability of Becoming a Multinational Group

Dependant variable: Become a Multinational Group

(1) (2) (3) (4)

Variables

Avg Subsidiary L (log) 0.003* 0.003* 0.002* 0.001

(0.001) (0.001) (0.001) (0.001)

Avg Subsidiary K/L (log) 0.002** 0.002** 0.001*** 0.001***

(0.001) (0.001) (0.000) (0.000)

Avg Subsidiary Sale (log) 0.008*** 0.008*** 0.006*** 0.007***

(0.002) (0.002) (0.001) (0.001)

No. Layers 2 0.013** 0.013** 0.013*** 0.012***

(0.005) (0.005) (0.004) (0.004)

No. Layers 3 0.040*** 0.041*** 0.037*** 0.036***

(0.011) (0.012) (0.011) (0.011)

No. Layers 4 0.044*** 0.044*** 0.044*** 0.048***

(0.013) (0.013) (0.013) (0.014)

No. Layers 5 0.066 0.066 0.061 0.058

(0.039) (0.040) (0.041) (0.043)

No. Layers 6 + 0.080 0.080 0.087 0.075

(0.061) (0.059) (0.059) (0.056)

28

Affiliates: 2-3 0.007*** 0.008*** 0.005*** 0.005***

(0.002) (0.002) (0.002) (0.001)

Affiliates: 4-5 0.016** 0.017** 0.011* 0.012*

(0.007) (0.007) (0.006) (0.006)

Affiliates: 6-20 0.048*** 0.050*** 0.043*** 0.044***

(0.014) (0.015) (0.013) (0.014)

Affiliates: 21-50 0.085* 0.092** 0.070* 0.071*

(0.041) (0.041) (0.038) (0.037)

Affiliates: 51-200 0.107 0.130 0.146 0.205

(0.116) (0.108) (0.121) (0.128)

Industry Diversity 0.043*** 0.042*** 0.022*** 0.017**

(0.006) (0.005) (0.007) (0.007)

Obervations 44,159 44,159 44,159 44,159

R-Squared 0.036 0.043 0.064 0.1

Year FE YES YES YES YES

Sector FE NO YES YES YES

Country FE NO NO YES YES

Sector Comp NO NO YES YES

CountryXSector FE NO NO NO YES Note: The statistical significance is as follows: * significant at 10% ; ** significant at 5%, *** significant at 1%. OLS regression on group level. The dependent variable is a dummy taking on a value of 1 if the group becomes a multinational in our sample period (and 0 if it is never multinational). Avg Subsidiary L (log) is the average log of the number of employees of the subsidiary in a group, Avg Subsidiary _K/L (log) is the average log of the capital intensity of the subsidiary in a group, Avg Subsidiary_Sales (log); is the average log of the sales of the subsidiary in a group. Affiliates is the number of subsidiaries in a business group. No. of Layers is the number of Layers of the business group; Industry Diversity is an index measuring the diversity of the group based on NACE 4. Standard errors are clustered at the Headquarters level. Data are from the Amadeus (Bureau Van Dijk) dataset, 2001-2007. The subsample considered is the one described in Table 1. However, only two types of business groups are considered—those that become multinational and those that were never multinational (groups always having a multinational status in the study period are dropped).

5 The First Foreign Acquisition

We now examine how a group becomes multinational—what the first

acquired subsidiary of these switching groups looks like. We analyse how the

first foreign subsidiary compares to the existing domestic subsidiaries: (i)

geographically—whether it is close or distant from the domestic group’s

country; (ii) the layer at which it enters within the hierarchy of the group; and

(iii) the industry in which it performs its activity. By so doing, we hope to shed

light on some of the reasons that a business group might choose to go

multinational.

29

Table 15 reports how domestic groups become multinational. More

specifically, it shows where these groups chose to acquire their first

subsidiaries. Note that the distribution of switchers across countries mirrors

the distribution of groups, as seen in Table 2. Countries with more groups

tend to have a larger number of switchers. This is expected, and it ensures

that the country composition in previous sections will be reflected in the

present analysis.

As reported in Table 15, many business groups acquire their first

subsidiary in neighbouring regions or in regions that are legally and culturally

similar to their own. This is in line with standard gravity model results in the

trade literature. Business groups in Spain, for example, acquire subsidiaries

mostly in Portugal, which aligns with the idea of acquiring firms in close

geographic proximity. Similarly, French groups enter Belgium (both culturally

close and neighbouring), Spain (bordering country) and countries

characterized by rich and large markets, such as Germany, the UK and Italy

(with which it also retains some historical ties). Many of these groups also

acquire their first subsidiary in Germany. This comes as no surprise given the

large market and hub for industrial production that Germany offers. We also

observe that business groups in a large number of countries enter the

Netherlands, also perhaps due to its proximity to Germany and its sea

access to the Nordic countries.

Table 15. Probability of Becoming a Multinational Group

Note: The table shows domestic groups that make their first foreign acquisition (switchers) in the time period studied (2001-2007). To maintain consistency with the rest of the tables in terms of countries represented, we report only switching groups (742 observations)

AT BE CH CZ DE DK EE ES FI FR GB GR IT NL NO PL PT RO SE

Austia AT 0 0 0 0 6 0 0 0 0 0 1 0 0 0 0 0 0 0 0 7

Belgium BE 1 0 0 1 12 3 0 2 0 29 5 3 2 17 1 3 0 1 0 80

Germany DE 18 0 5 1 0 3 0 5 0 3 24 0 0 15 0 9 0 0 0 83

Spain ES 1 1 1 6 18 2 1 0 0 33 24 1 18 8 0 0 3 3 1 121

Finland FI 1 0 0 0 9 5 39 0 0 1 9 0 0 1 11 2 0 0 1 79

France FR 1 25 3 0 45 1 0 4 1 0 52 0 13 9 2 4 3 4 0 167

Italy IT 4 3 1 0 21 0 0 11 0 14 26 3 0 1 0 0 1 0 1 86

NetherlandsNL 1 2 1 0 9 0 0 2 0 0 3 0 0 0 0 2 0 0 1 21

Portugal PT 0 0 0 0 0 0 0 4 0 1 0 0 0 0 0 1 0 0 0 6

Sweden SE 0 1 0 0 5 17 7 2 8 2 14 0 2 2 29 2 1 0 0 92

Total BGs 27 32 11 8 125 31 47 30 9 83 158 7 35 53 43 23 8 8 4 742

Total

BGs

Switching BG's Country of first move

Subsidiary's Country of First MoveBG HQ's

Nationality

30

belonging to those 20 countries that had the highest concentration of business groups in our original sample (as reflected in Table 2). BG HQ’s Nationality is the ultimate holder’s nationality, while Subsidiary’s Country of First Move reflects the foreign country into which the business group first decided to expand. Data are from the Amadeus (Bureau Van Dijk) dataset.

The group’s decision to become multinational can be made at various

levels within the group: the acquisition could be done by the headquarters or

by one of the close subsidiaries; a decision at the top level could imply a

“corporate-level decision”; or a decision by a different subsidiary could

represent an independent move with respect to the rest of the group. In the

latter case, the acquisition would also change the group structure, making it

more hierarchical by increasing the number of layers.

Table 16 reports the tabulation of the mode of the level in the domestic

part of the group and the mode of the level of the entrant that makes the

group multinational (we use the mode since, in a few cases, there are more

than one new foreign subsidiaries). As the table reflects, becoming a

multinational group appears to be mainly a centralized decision by the

headquarters or one of its close subsidiaries. The mode of the entrant

making the group multinational is at a higher level than the mode of the layer

of domestic subsidiaries. There are 583 groups for which the mode of the

layer of subsidiaries belonging to the group is level 1. In 344 of these cases,

the mode of the foreign subsidiary that first makes the group multinational

also comes in at level 1, at the top—that is, directly under the headquarters.

However, in 228 of the cases, the subsidiaries that make the group

multinational come in on level 2. The large number of observations on the

main diagonal, which identifies groups whose new foreign subsidiary comes

in at the typical level of existing domestic subsidiaries, suggests that the

usual subsidiaries making acquisitions in the group are also responsible for

the group internationalization.

Table 16. Layer of Entry of 1st acquired subsidiary

# of Switching Groups : Layer of entry of subsidiary making group Multinational

Laye

r o

f d

om

esti

c su

bsi

dia

ries

o

f gr

ou

p 1 2 3 4 5 # Switchers

1 344 228 9 1 1 583

2 9 43 10 1 1 64

3 1 0 2 3 0 6

31

4 0 0 1 0 0 1

354 271 22 5 2 654 Note: The table reports the mode of the level in the domestic part of the group and the mode of the level of the entering subsidiary that makes the group multinational (the mode is used, as in few cases, there are more than one new foreign subsidiaries). The sample considered are the subsidiaries that made domestic groups multinational during the time period studied (2001-2007). Data used are from the Amadeus (Bureau Van Dijk) dataset.

Differences in terms of layer of entry could also be related to some

characteristics of the group or of the subsidiaries belonging to the group. In

order to take these elements into account, we run a simple OLS regression

on an affiliate level, having as our dependent variable the layer of the group

to which the subsidiary belongs and as our independent variables several

group and subsidiary characteristics (Table 17). The variable of interest is the

dummy “New foreign Sub,” which identifies the firm making the group

multinational. This regression largely confirms what is shown in the Table 16:

the level of entry of the foreign subsidiary is higher than, but quite close to,

that of the typical firm in the group. This implies that the decision is made at

the top, and, therefore, it reflects a “corporate-level decision” that can affect

the whole group.

Table 17. Foreign subsidiaries’ level of entry

(1) (2) (3) (4) (5)

VARIABLES

New foreign Sub 0.327*** 0.334*** 0.321*** 0.221** 0.227** (0.036) (0.037) (0.032) (0.079) (0.087)

avg_lemp 0.006 0.008 -0.005 0.005 -0.005

(0.018) (0.018) (0.018) (0.029) (0.031)

avg_lcap_emp 0.001 0.005 0.005 -0.006 -0.005

(0.007) (0.006) (0.005) (0.009) (0.007)

avg_lturn -0.019 -0.023 -0.017 -0.041 -0.038

(0.018) (0.016) (0.018) (0.030) (0.030)

Layers_2 0.089*** 0.067** 0.061** 0.123*** 0.122***

(0.020) (0.025) (0.025) (0.032) (0.034)

Layers_3 0.315*** 0.302*** 0.301*** 0.378*** 0.373***

(0.049) (0.048) (0.048) (0.053) (0.053)

Layers_4 0.705*** 0.686*** 0.680*** 0.746*** 0.762***

(0.086) (0.072) (0.080) (0.141) (0.140)

Layers_5 0.416* 0.384* 0.391* 0.448* 0.478**

(0.223) (0.209) (0.207) (0.222) (0.202)

Layers_6more 0.371*** 0.358*** 0.383*** 0.351** 0.334**

(0.122) (0.117) (0.116) (0.126) (0.140)

32

BG_Size_2_3 0.293*** 0.329*** 0.393*** 0.197 0.265

(0.051) (0.057) (0.065) (0.162) (0.177)

BG_Size_4_5 0.371*** 0.400*** 0.450*** 0.263 0.314*

(0.043) (0.040) (0.046) (0.161) (0.179)

BG_Size_6_20 0.386*** 0.431*** 0.469*** 0.210 0.262

(0.055) (0.045) (0.055) (0.154) (0.158)

BG_Size_21_50 0.613*** 0.653*** 0.674*** 0.299** 0.344**

(0.112) (0.141) (0.145) (0.118) (0.123)

Sector_Div -0.027 -0.027 -0.010 0.047 0.048

(0.030) (0.025) (0.030) (0.052) (0.074)

Observations 903 903 903 903 903

R-squared 0.282 0.311 0.339 0.545 0.574

Year FE YES YES YES YES YES

Sector FE NO YES YES YES YES

Country FE NO NO YES YES YES

CountryXSector FE NO NO NO YES YES

Sector Composition NO NO NO NO YES

Robust standard errors in parentheses

*** p

33

to diversify its activities with the aim of exploiting other countries’ comparative

advantage. Our finding is consistent with that of Alforo and Charlton (2009),

who show that once we get to detailed enough industry codes (4-digits),

much of the FDI proves to be vertical.

Table 18. Industry of Entry of 1st acquired subsidiary

Panel A Group Sector

( subsidiary mode)

Panel B

Ultimate Holder's Sector

# Switchers Percentage # Switchers Percentage

Different Sector 419 64% 514 79%

Same Sector 235 36% 140 21%

Total # of Switchers 654 654

The findings presented in this section offer evidence about why a

group goes multinational. The data suggest that a multinational expansion of

a group is a rather centralized process, initiated by the ultimate owner itself

or by one of the closest subsidiaries (layer 1), which means that it is a

“corporate-level decision.” This comes as no surprise, as the important

strategic decision to expand into a different country probably has to take into

account the group structure and characteristics. However, the expansion

seems to also further diversify the group in terms of economic activity: the

first foreign subsidiary is usually from a different sector than the major part of

the group and from a different sector than that of its ultimate owner. This

suggests that group multinational expansion might be driven by the desire to

acquire particular inputs from a foreign country or to make use of a country’s

comparative advantage.

6 Conclusion

An extensive literature provides evidence on the advantages of

affiliate performance when belonging to business groups (BGs), as opposed

to being standalones, as well as the benefits of multinational enterprises over

domestic firms. However, no attempt had been made so far to connect these

two streams of the literature and to analyse business groups from a

multinational/domestic perspective. This paper aims to bridge the two

34

different literatures by exploiting the Amadeus dataset, together with the

business group information from the dataset of Bena, Fons-Rosen and

Hanousek (2009). We look at sales performance of firms that belong to a

multinational business group and analyse possible drivers that increase the

likelihood of becoming a multinational group. We then look at how the

acquisition of multinational status affects the group structure and activities by

focusing on the first foreign subsidiary acquired.

Multinational groups appear to differ substantially from domestic

groups: they are usually larger and more hierarchical in terms of group

structure; and their subsidiaries also appear to be superior to subsidiaries of

domestic groups in terms of both size and sales performance. More

specifically, we find that firms belonging to multinational groups have 15%

higher sales after accounting for differences in observables at the group and

firm levels.

Transition to multinational status is related to group characteristics.

Groups that are larger (with more affiliates) and more hierarchical (having

many layers in the group), and those that have higher sectoral diversity

(industry mix of the affiliates) are more likely to become multinational. This

suggest that there is a strong selection into becoming a multinational.

The transition to multinational status appears to be a rather centralized

decision. The foreign subsidiary is, in most of cases, acquired by the ultimate

owner itself or by a subsidiary directly controlled by it. This implies that the

decision does not have substantial effects on the business group structure.

However, foreign subsidiaries appear to differ from the group with respect to

their main economic activity: only in a minority of cases does the sector of the

first foreign subsidiary coincide with the sector of domestic subsidiaries or

with that of the headquarters. This provides some preliminary evidence that

the decision to acquire a foreign subsidiary might be driven by the group’s

desire to expand its set of activities, possibly gaining access to valuable

inputs to exploit a country’s relative advantage in some sector.

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